Information system architectures for syndromic surveillance.
INTRODUCTION: Public health agencies are developing the capacity to automatically acquire, integrate, and analyze clinical information for disease surveillance. The design of such surveillance systems might benefit from the incorporation of advanced architectures developed for biomedical data integration. Data integration is not unique to public health, and both information technology and academic research should influence development of these systems. OBJECTIVES: The goal of this paper is to describe the essential architectural components of a syndromic surveillance information system and discuss existing and potential architectural approaches to data integration. METHODS: This paper examines the role of data elements, vocabulary standards, data extraction, transport and security, transformation and normalization, and analysis data sets in developing disease-surveillance systems. It then discusses automated surveillance systems in the context of biomedical and computer science research in data integration, both to characterize existing systems and to indicate potential avenues of investigation to build systems that support public health practice. RESULTS: The Public Health Information Network (PHIN) identifies best practices for essential architectural components of a syndromic surveillance system. A schema for classifying biomedical data-integration software is useful for classifying present approaches to syndromic surveillance and for describing architectural variation. CONCLUSIONS: Public health informatics and computer science research in data-integration systems can supplement approaches recommended by PHIN and provide information for future public health surveillance systems.